- 1 How do you maintain a deployed model?
- 2 How do you evaluate models after deployment?
- 3 What are good reasons to keep monitoring your model performance after it is deployed into a service?
- 4 How do I retrain my model with new data?
- 5 What is deploy model?
- 6 Why do we need model monitoring?
- 7 Why is modeling monitoring important?
- 8 How do you deploy a trained model?
How do you maintain a deployed model?
- Actions 1: Retrain the model with new data.
- Actions 2: Retrain the model with additional features.
- Actions 3: Develop a new model from scratch.
How do you evaluate models after deployment?
After the models are deployed in production, I’d monitor the following: (1) The same metric you used to evaluate the performance of your model, in some cases it is accuracy, or it could be precision, recall, RMSE.
What are good reasons to keep monitoring your model performance after it is deployed into a service?
Model Monitoring is an operational stage in the machine learning life cycle that comes after model deployment, and it entails ‘monitoring’ your ML models for things like errors, crashes, and latency, but most importantly, to ensure that your model is maintaining a predetermined desired level of performance.
How do I retrain my model with new data?
Rather retraining simply refers to re-running the process that generated the previously selected model on a new training set of data. The features, model algorithm, and hyperparameter search space should all remain the same. One way to think about this is that retraining doesn’t involve any code changes.
What is deploy model?
Model deployment is simply the engineering task of exposing an ML model to real use. The term is often used quite synonymously with making a model available via real-time APIs.
Why do we need model monitoring?
The primary goal of monitoring here is to flag any data quality issue, either from the client or due to an unhealthy data pipeline, before the data is sent to your model (which would generate unreliable predictions in response). In some cases, your streaming data pipeline will ingest data from multiple sources.
Why is modeling monitoring important?
Model monitoring helps you to track performance shifts. As a result, you can determine how well the model performs. Also, it helps you to understand how to debug effectively if something goes wrong. The most straightforward way to track the shift is constantly evaluating the performance on real-world data.
How do you deploy a trained model?
How to deploy Machine Learning/Deep Learning models to the web
- Step 1: Installations.
- Step 2: Creating our Deep Learning Model.
- Step 3: Creating a REST API using FAST API.
- Step 4: Adding appropriate files helpful to deployment.
- Step 5: Deploying on Github.
- Step 6: Deploying on Heroku.